Reversing cognitive-motor impairments in patients having a neuro-degenerative disease using a computational modeling approach to deep brain stimulation programming

ABSTRACT

A system and method may provide for conducting a stimulation of anatomic regions to treat a neuromotor, neurocognitive or neuromotor and neurocognitive disorder, according to which stimulation, motor regions are stimulated, while creep of current to non-motor regions is minimized. Stimulation parameters may be selected based on tests of motor function, tests of cognitive function, and tests of a combination of motor and cognitive functions.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation-in-part application ofInternational Patent Application No. PCT/US10/58770, filed Dec. 2, 2010,which claims priority to U.S. Provisional Patent Application No.61/265,782, filed Dec. 2, 2009, the entire contents of each of which ishereby incorporated by reference herein.

GOVERNMENT RIGHTS

Using the specific language required by 37 C.F.R. §401.14(f)(4): Thisinvention was made with government support under grant numbers R01NS058706 and R01 NS059736 awarded by the National Science Foundation.The government has certain rights in the invention.

FIELD OF THE INVENTION

The present invention relates to a system and method for stimulatinganatomic regions and/or for selecting parameters for such stimulation.

BACKGROUND

Deep brain stimulation (DBS) in the subthalamic nucleus (STN) and otherforms of neuromodulation are effective and safe surgical procedures thathave been shown to reduce the motor dysfunction of advanced Parkinson'sdisease (PD) patients. Bilateral DBS refers to stimulation on both sidesof the brain, while unilateral DBS refers to stimulation on one side ofthe brain. Bilateral and unilateral DBS typically target one of threeareas, including the STN, GPI, VIM. Bilateral STN DBS has beenassociated with declines in cognitive and cognitive-motor functioning.DBS is similarly used to treat other neuro-degenerative diseasesincluding cognitive, motor, and cognitive-motor disorders, but presentlyused stimulation parameters result in detrimental side effects.

SUMMARY

Activities of daily living are typically performed under modestlycomplex conditions and have cognitive and motor components that areperformed simultaneously (Cahn-Weiner, D. A. et al., “Tests of executivefunction predict instrumental activities of daily living incommunity-dwelling older individuals,” Appl. Neuropsychol. 9, 187-91(2002) (hereinafter “Cahn-Weiner et al., 2002”), the entire contents ofwhich is hereby incorporated by reference herein). Frontal and executivedysfunction in the elderly and PD patients without DBS can be predictiveof cognitive and motor function during ADLs (Cahn-Weiner et al., 2002;Cahn, D. A. et al., “Differential contributions of cognitive and motorcomponent processes to physical and instrumental activities of dailyliving in Parkinson's disease,” Arch Clin Neuropsychol. 13, 575-83(1998) (hereinafter “Cahn et al., 1998”), the entire contents of each ofwhich is hereby incorporated by reference herein). Understanding how PDand DBS impact cognitive and motor function under conditions requiringgreater cognitive rigor and during the simultaneous performance ofcognitive and motor tasks may provide a more accurate assessment of theeffect of a set of stimulation parameters on cognitive and motorperformance when patients are completing “real world” tasks. Currentmethods of assessing cognitive and motor function in a clinicalenvironment may not be sufficiently demanding or sensitive enough toreveal changes in cognitive or motor performance that occur when eithercomponent of a task is increased. There is an emerging body ofliterature indicating a paradox between the clinical improvements inmotor functioning associated with STN DBS and the patient andcaregiver's level of postoperative satisfaction (Agid, Y. et al.,“Neurosurgery in Parkinson's disease: the doctor is happy, the patientless so?,” J. Neural Transm. Suppl. 409-14 (2006); Schupbach, M. et al.,“Neurosurgery in Parkinson disease: a distressed mind in a repairedbody?,” Neurology 66, 1811-6 (2006) (hereinafter “Schupbach et al.,2006”); Schupbach, M. et al., “Psychosocial adjustment after deep brainstimulation in Parkinson's disease,” Nature Clinical Practice 4, 58-59(2008) (hereinafter “Schupbach and Agid, 2008”), the entire contents ofeach of which is hereby incorporated by reference herein).

Spread of current to non-motor areas of the STN may cause declines incognitive and cognitive-motor functioning. A study was performed toassess and compare the cognitive-motor performance in advanced PDpatients with bilateral STN DBS parameter settings determined clinically(Clinical), e.g., by subjective assessment such as asking a patient howthe patient feels or observation of side effects due to stimulation, andwith bilateral STN DBS parameter settings derived from apatient-specific computational model (Model), according to which currentcreep to non-motor regions was minimized or removed altogether. In thisregard, the conventional method of parameter selection did notcontemplate consideration of reduction of current creep to non-motorregions, but rather were selected largely on the subjective clinicalmeasures, such that if one or more symptoms improved to some degree andthere was no side effect noticed, then the parameters associated withthose results were deemed worthy of use, without consideration of effecton creep to the non-motor regions. It was also conventionally not knownthat the spread of current to non-motor regions would negatively affectmotor skill.

In the study, data were collected from 10 advanced PD patients, offmedication, under three DBS conditions: OFF, Clinical and Model basedstimulation. Clinical stimulation parameters had been determined basedon clinical evaluations and the parameters were stable, i.e., unchanged,for at least six months prior to study participation. Model basedparameters were selected to minimize the spread of current to non-motorportions of the STN using Cicerone DBS software (See Miocinovic S. etal., “Cicerone: stereotactice neurophysiological recording and deepbrain stimulation electrode placement software system,” Acta NeurochirSuppl., 97, 561-7 (2007) (hereinafter “Miocinovic et al., 2007”), whichis incorporated by reference herein). That is, in the study, andaccording to an example embodiment of the invention, software is usedthat provides a 2D or 3D visualization of patient images, DBSelectrodes, and/or calculated estimated/predicted volumes of activationfor specified stimulation parameters. Based on those visualizations, anoperator is able to modify the parameters until a VTA is provided thathas minimal current spread to non-motor portions of the brain. In factsuch visualization and tinkering until selection of parameters for usemay be performed without the presence of the patient.

For each stimulation condition (OFF, Clinical, and Model), participantsperformed a working memory (n-back task) and motor task (force-tracking)under single- and dual-task settings. During the dual-task, participantsperformed the n-back and force-tracking tasks simultaneously. Clinicaland Model parameters were equally effective in improving the UnifiedParkinson's disease Rating Scale (UPDRS-III) scores relative to Off DBSscores, e.g., with respect to motor response as measured by theUPDRS-III. The average improvement in off medication UPDRS-III scoresfor both parameter settings, 46 percent, is within the range ofimprovement typically reported in long-term studies with bilateral STNDBS in advanced PD patients (Abelson, J. L. et al., “Deep brainstimulation for refractory obsessive-compulsive disorder,” BiolPsychiatry. 57, 510-6 (2005); Kumar, R. et al., “Long-term follow-up ofthalamic deep brain stimulation for essential and parkinsonian tremor,”Neurology. 61, 1601-4 (2003); Rodriguez-Oroz, M. C. et al., “Bilateraldeep brain stimulation in Parkinson's disease: a multicentre study with4 years follow-up,” Brain 128, 2240-9 (2005); Weaver, F. M. et al.,“Bilateral deep brain stimulation vs best medical therapy for patientswith advanced Parkinson disease: a randomized controlled trial,” Jama.301, 63-73 (2009), the entire contents of each of which is herebyincorporated by reference herein). The inventors discovered that then-back and force-tracking tasks, administered as described herein toobtain the test results described herein, provide a better context inwhich activities of daily living are completed, as most ADLS have acognitive and motor component, and therefore provide a better measurethan UPDRS for determining effectiveness of stimulation parameters.

Single-task working memory declines, in the 2-back condition, weresignificantly less under Model compared to Clinical DBS settings. Underdual-task conditions, force tracking was significantly better with Modelcompared to Clinical DBS. These results indicate that the cognitive andcognitive-motor declines associated with bilateral STN DBS may bereversed, without compromising motor benefits, by utilizing stimulationparameters that minimize current spread into non-motor regions of theSTN. Theoretically, it is conceivable that there may be a task that is100% motor related, without any cognitive function required, in whichcase it may occur that there would be no performance difference betweenClinical and Model parameters, but almost no ADLs are purely motor.

Indeed, the transmission of pathological information within the basalganglia thalamocortical circuits is thought to underlie the symptoms ofPD (Albin, R. L. et al., “The functional anatomy of basal gangliadisorders,” Trends Neurosci 12, 366-375 (1989); DeLong, M. R., “Primatemodels of movement disorders of basal ganglia origin,” Trends. Neurosci.13, 281-285 (1990); Llinas, R. R. et al., “Thalamocortical dysrhythmia:a neurological and neuropsychiatric syndrome characterized bymagnetoencephalography,” Proc. Natl. Acad. Sci. U.S.A. 96 (1999);Timmermann, L. et al., “The cerebral oscillatory network of parkinsonianresting tremor,” Brain 126, 199-212 (2003); Vitek, J. L. et al.,“Physiology of hypokinetic and hyperkinetic movement disorders: modelfor dyskinesia,” Ann. Neurol. 47, S131-S140 (2000), the entire contentsof each of which is hereby incoporated by reference herein). It isbelieved that DBS acts to regularize activity within the motor circuitthereby reducing the passage of pathological information from thepallidum (Grill, W. M. et al., “Deep brain stimulation creates aninformational lesion of the stimulated nucleus,” Neuroreport 15, 1137-40(2004); Guo, Y. et al., “Thalamocortical relay fidelity varies acrosssubthalamic nucleus deep brain stimulation protocols in a data-drivencomputational model,” J Neurophysiol. 99, 1477-92 (2008); Hashimoto, T.et al., “Stimulation of the subthalamic nucleus changes the firingpattern of pallidal neurons,” J Neurosci. 23, 1916-23 (2003), the entirecontents of each of which is hereby incoporated by reference herein).The spread of current to non-motor regions of the STN is likely todisrupt the spread of non-pathological information from these non-motorregions of the STN. Disruption of information processing in thesenon-motor regions may be responsible for the DBS related cognitive-motordeclines observed under dual-task conditions. The loss of transmittedinformation or information processing capabilities may not produce adeficit in cognitive function following unilateral procedures (Alberts,J. L. et al., “Bilateral subthalamic stimulation impairs cognitive-motorperformance in Parkinson's disease patients,” Brain 131, 3348-60 (2008)(herein after “Alberts et al., 2008”), the entire contents of which ishereby incporperated by reference) or when the patients are able tofocus all of their attention on the performance of a cognitive or motortask alone, as is the case during most clinical examinations. However,as the cognitive demands of the task increase, information processingdemands increase. Therefore, under bilateral STN DBS with conventionalClinically determined stimulation parameters, which results in spread ofcurrent to non-motor regions, such current spread may compromisecognitive-motor functioning. Cognitive resources on which patients mayattempt to draw may now be even more compromised as a result ofbilateral disruption of non-motor pathways. The Model parametersaccording to the present invention, set to avoid spread to non-motorregions may minimize or avoid such further degeneration of cognitiveresources.

Further in this regard, the focus of clinical programming has been onthe motor response, and unless non-motor side effects are readilyapparent, they are generally not detected; particularly those that mayonly arise under more complex testing conditions. In turn, unintentionalover-stimulation can occur when the stimulus amplitude at a therapeuticcontact is adjusted to be just below threshold for motor side effects,related to assumption that more stimulation is better than less.However, the study conducted by the inventors indicates that Modelparameters resulted in similar improvements in clinical ratings andminimized cognitive-motor declines under dual-task conditions comparedto Clinical settings, while using significantly less power (cf Table 4below). Model parameters were selected to both focus a volume of tissueactivated (VTA) on the target region and to be energy efficient.Previous clinical studies have found no significant benefit from usingstimulation frequencies greater than 100-130 Hz (Moro, E. et al., “Theimpact on Parkinson's disease of electrical parameter settings in STNstimulation,” Neurology 59, 706-13 (2002) (hereinafter “Moro et al.,2002”; Rizzone, M. et al., “Deep brain stimulation of the subthalamicnucleus in Parkinson's disease: effects of variation in stimulationparameters,” J. Neurol. Neurosurg. Psychiatry 71, 215-9 (2001), theentire contents of each of which is hereby incorporated by referenceherein), and from the biophysical perspective of axonal activation themost energy efficient pulse width available in the MedtronicSoletra/Kinetra DBS system is 60 μs in a monopolar configuration (Butsonand McIntyre, 2007; Sahin, M. et al., “Non-rectangular waveforms forneural stimulation with practical electrodes,” J. Neural Eng. 4, 227-33(2007), the entire contents of each of which is hereby incoporated byreference herein). Therefore, the Model DBS parameters according to thepresent invention may be selected with these constraints, resulting inreduced power consumption that could help to minimize the threat ofstimulation induced tissue damage and prolong battery life expectancy.

In addition to better overall cognitive-motor performance associatedwith Model parameters, the amount of power consumed was, on average,less than half of the Clinical settings.

Although the present invention is described in relation to Parkinson'sDisease and the STN, the methods and systems of the present inventioncan be used by patients suffering from medical disorders. In preferredembodiments, the medical disorders are characterized by abnormal motorfunction, such as in patient's limbs (upper and/or lower extremities).The medical disorder can be a neurological disorder (i.e., a disorder ofthe patient's nervous system). In certain embodiments, the neurologicaldisorder is a neuromotor or neurocognitive disorder that results inabnormal motor function and that is characterized by irregular motorcortical output including, for example, output from the cerebellumand/or supplementary motor area (“SMA”) of the cortex; and irregularsub-cortical output from regions that contribute to motor function in apatient such as, for example, the basal ganglia, the subthalamic nucleusand/or the thalamus.

The methods have application to mammalian patients, including humanssuffering from the above-described disorders. In certain embodiments,the neuromotor or neurocognitive disorders are degenerative in nature.Exemplary disorders include PD, Alzheimer's Disorder, dementia,Parkinsonian syndrome, essential tremor, multiple sclerosis (MS),amyotrophic lateral sclerosis (ALS), traumatic brain injury, stroke,multiple system atrophy (MSA), and dystonia.

Embodiments of the present invention are directed to a system and methodfor stimulating an anatomical region in a stimulation procedure in whichless than 10% of non-motor regions (i.e., regions not associated withmotor function), e.g., of the brain, are stimulated. In an exampleembodiment, one or more of the zona incerta, lenticular fasciculus, andmotor region of the globus pallidus and internal capsule are stimulated,while less than 10% of non-motor anatomical-neural regions of the globuspallidus or STN are stimulated.

In an example embodiment of the present invention, an anatomical regionis stimulated in a stimulation procedure in which at least one of thezona incerta, lenticular fasciculus, and motor region of the globuspallidus and internal capsule are stimulated, while current is notspread to, and therefore there is no stimulation of, any of thecorticospinal tract (CS), corticobulbar tract (CB), and the non-motorregions of the globus pallidus (GP) and internal capsule.

For example, a target VTA may be created, for example, according tomethods described in U.S. patent application Ser. No. 12/266,394,entitled “3D Atlas Fitting Using MicroElectrode Recordings” and filedNov. 6, 2008, in U.S. Provisional Patent Application Ser. No.61/120,006, entitled “System and Method to Define Target Volume forStimulation in the Brain” and filed Dec. 4, 2008, in InternationalPatent Application No. PCT/US09/066821 entitled “System and Method toDefine Target Volume for Stimulation in the Brain” and filed Dec. 4,2009, in U.S. patent application Ser. No. 12/869,159, entitled “Systemand Method to Estimate Region of Tissue Activation” and filed Aug. 26,2010, and in International Patent Application No. PCT/US 1046772,entitled “System and Method to Estimate Region of Tissue Activation” andfiled Aug. 26, 2010, the entire contents of each of which is herebyincorporated by reference in its entirety, such that they include no orminimal spread of current to the non-motor regions.

Example embodiments of the present invention are directed to a systemand method for selection of stimulation parameters for treatment ofneuro-degenerative disorders, such as neuro-cognitive and/or neuro-motordisorders based on results of tests of cognitive function, tests ofmotor function, and a combination of such tests, performed by thepatient to which stimulation is to be performed using the selectedstimulation parameters. The stimulation may be performed using implantedelectrodes. In an example embodiment, the test results may be used forselection of stimulation parameters that minimize creep of current tonon-motor anatomic regions, e.g., of the brain. In an exampleembodiment, the n-Back test may be used as the test for testingcognitive function, a force-maintenance task may be used as the test fortesting motor function, and a test where a patient is subjected to boththe n-Back test and the force-maintenance task simultaneously may beused as the combination as a “dual task.”

In alternative example embodiments of the present invention, the motorand cognitive testing discussed throughout the present application maybe performed using motor, cognitive, and/or motor-cognitive testsdescribed in U.S. Provisional Pat. App. Ser. No. 61/262,662, filed Nov.19, 2009 (“the '662 application”) and/or in International Pat. App. No.PCT/US10/57453, filed Nov. 19, 2010 (“the '453 application”), the entirecontents of each of which is hereby incorporated by reference herein.Those tests may be administered and the test results captured andrecorded, for example, as described in the '662 application and/or the'453 application.

U.S. Provisional patent Application No. 61/409,693, entitled “ImprovingPostural Stability with STN DBS” and filed Nov. 3, 2010, the entirecontents of which is hereby incorporated by reference in its entiretydescribes further application and details of the features describedherein concerning stimulation parameters selected for minimizing spreadof current to and stimulation of non-motor anatomical regions.

Example embodiments of the present invention are directed to a computersystem configured to determine stimulation parameters based on theabove-described tests, e.g., to minimize creep of current to non-motoranatomical regions. That is, to determine stimulation parameters, thesystem may evaluate various parameter settings using objective andquantitative test that have cognitive and motor components.

Programming DBS devices for maximal clinical benefit and minimal sideeffects can be a difficult and time consuming process, requiring ahighly trained and experienced individual to achieve desirable results(Hunka, K. et al. “Nursing time to program and assess deep brainstimulators in movement disorder patients,” J. Neurosci. Nurs. 37,204-10 (2005); Moro, E. et al., “Subthalamic nucleus stimulation:improvements in outcome with reprogramming,” Arch. Neurol. 63, 1266-72(2006) (hereinafter “Moro et al., 2006”), the entire contents of each ofwhich is hereby incoporated by reference herein). While guidelines existon stimulation parameter settings that are typically effective (Moro etal., 2002; Rizzone et al., 2001; Volkmann, J. et al., “Basic algorithmsfor the programming of deep brain stimulation in Parkinson's disease,”Mov. Disord. 21 Suppl. 14, S284-9 (2006), the entire contents of each ofwhich is hereby incorporated by reference herein), these vary frompatient to patient and it is not practical to clinically evaluate eachof the thousands of stimulation parameter combinations that are possiblein order to optimize DBS in each patient. As a result, the therapeuticbenefits achieved with DBS are strongly dependent on the intuitive skilland experience of the clinician performing the programming (Moro et al.,2006) and the amount of time each programmer can allocate to thatpatient.

Rather than relying solely on intuition and experience, clinical DBSprogramming according to the present invention can be augmented withvisualization of electrode location and theoretical calculation of anoptimal VTA. Software technology can provide an initial starting pointfor the clinical programming process, thereby focusing patient testingon a select range of stimulation settings where an abbreviated versionof the dual-task paradigm could be performed to evaluate cognitive andmotor function. For example, the VTA may be visualized before theprogramming user even sees the patient. The programming user canessentially test a host of parameter sets using the software rather thanhaving to actually apply those parameters to stimulation of the patient.The parameters that are most likely ineffective may therefore beeliminated to begin with. Additionally, the software itself may beprogrammed with parameter sets that are most likely ineffective due to,for example, current creep to non-motor regions, and may accordinglyoutput suggested sets of parameter settings. The dual-task paradigmcould concentrate clinical resources on maximizing clinical outcomes andminimize time consuming searches through the DBS parameter space(contact, voltage, pulse width, frequency).

According to an example embodiment of the present invention, cognitiveand motor performance may be evaluated simultaneously, e.g., byadministering the above-described tests, during DBS programming whilevisualizing VTAs associated with specific DBS parameters. This maymitigate the described paradoxical situation between the clinicalimprovements in motor functioning and the patient and caregiver's levelof postoperative satisfaction. That is, by modifying DBS parametersbased on the test results, the patient satisfaction to the DBSprogramming using the resultant stimulation parameters may be consistentwith the motor benefits.

In this regard, by visualizing the various VTAs while simultaneouslyassessing the cognitive and motor performance as described herein, themedical clinician may be able to rank various VTAs according to suchperformance and modify the target VTAs until stimulation parameters areprovided for a closely matching estimated VTA that produces the bestcognitive and motor performance. The clinician may input notes inassociation with VTAs, which notes identify and/or rank the assessedcognitive and motor performance. The system may include a graphical userinterface (GUI) that displays an anatomical model, e:g., of the patientbrain and implanted leadwire, that further displays with respect to thedisplayed model one or more areas corresponding to explored VTAs, andthat further displays note icons representing the input notes associatedwith such VTAs and displayed such that it is indicated with which VTAsthe corresponding notes are associated. Responsive to selection of thenote icons, the system and method may display the notes.

According to an example embodiment of the present invention, theclinician may input a score associated with the explored VTAs based onthe cognitive and motor function, and for those VTAs for which a scorenot meeting a predetermined threshold, the system may treat the VTA asone associated with a side effect. The system and method may visuallyindicate which explored VTAs are associated with side effects and whichare not. Such graphics may be further considered by the clinician toultimately make a final selection of the stimulation parameters to use.

It is noted that other factors may be used in selection of stimulationparameters, and, while the assessed cognitive and motor performance maybe considered, the parameters resulting in the very best performance arenot necessarily selected. Accordingly, parameters which result in a goodperformance, but not necessarily the best, may be selected.

In an example, a system user may select initial parameter settings byinputting a target VTA with minimal current creep to the non-motorregions and obtaining settings that provide an estimated VTA closelymatching the target VTA. If the patient performs poorly on theadministered tests, a new target VTA may be drawn with even less creepto the non-motor regions or at further distance from such regions, etc.As noted above, the cognitive and motor performance may be tested whilevisualizing the VTAs associated with particular parameters. Theclinician can keep tweaking the target VTAs according to the testperformance. That is, the clinician will be able to quickly see theresults of the performance and those results relate to the position andsize of the VTA.

Example embodiment of the present invention are directed to a computersystem configured to provide a GUI via which the computer system mayobtain user input according to which the computer system is configuredto output a representation of a target VTA. The user input may bestimulation parameters and/or references to anatomical points, e.g., ofa perimeter of the target VTA. The computer system may be configured tofurther provide a GUI via which the user may adjust parameters or pointsof the target VTA to obtain a desired target VTA. For example, thetarget VTA may be one that avoids non-motor anatomical regions to theextent described above. In an example embodiment of the presentinvention, the system may determine stimulation parameter settings thatare estimated to provide a VTA that most closely matches the inputtarget VTA. In an example embodiment, the system may operate under acondition that it limits the most closely matching estimated VTA to onethat does not protrude beyond any point of the outer perimeter of thetarget VTA (even though there may be such a VTA that is a closer matchto the target VTA).

For obtaining input for the generation of, and for generating, thetarget VTAs and/or for determining estimated VTAs, and/or for recordingand visually outputting notes or indications of notes concerningcognitive and motor performance associated with VTAs, the system andmethod of the present invention may, for example, use processesdescribed in U.S. patent application Ser. No. 12/454,330, filed May 15,2009 and entitled “Clinician Programmer System and Method forCalculating Volumes of Activation” (“the '330 application”), in U.S.patent application Ser. No. 12/454,312, filed May 15, 2009 and entitled“Clinician Programmer System and Method for Calculating Volumes ofActivation for Monopolar and Bipolar Electrode Configurations” (“the'312 application”), in U.S. patent application Ser. No. 12/454,340,filed May 15, 2009 and entitled “Clinician Programmer System and Methodfor Steering Volumes of Activation” (“the '340 application”), in U.S.patent application Ser. No. 12/454,343, filed May 15, 2009 and entitled“Clinician Programmer System Interface for Monitoring Patient Progress”(“the '343 application”), and in U.S. patent application Ser. No.12/454,314, filed May 15, 2009 and entitled “Clinician Programmer Systemand Method for Generating Interface Models and Displays of Volumes ofActivation” (“the '314 application”), the content of each of which ishereby incorporated herein by reference in its entirety.

Example embodiments of the present invention are directed to a computersystem configured to monitor a patient performance during, and/or torecord results of, the tests described above, the results of which maybe used to select the stimulation parameters.

The computer system(s) may include one or more processors, which may beimplemented using any conventional processing circuit and device orcombination thereof, e.g., a Central Processing Unit (CPU) of a PersonalComputer (PC) or other workstation processor, to execute code provided,e.g., on a hardware computer-readable medium including any conventionalmemory device, to perform any of the methods described herein, alone orin combination. The one or more processors may be embodied in a serveror user terminal or combination thereof. The user terminal may beembodied, for example, a desktop, laptop, hand-held device, PersonalDigital Assistant (PDA), television set-top Internet appliance, mobiletelephone, smart phone, etc., or as a combination of one or morethereof. The memory device may include any conventional permanent and/ortemporary memory circuits or combination thereof, a non-exhaustive listof which includes Random Access Memory (RAM), Read Only Memory (ROM),Compact Disks (CD), Digital Versatile Disk (DVD), and magnetic tape.

Example embodiments of the present invention are directed to one or morehardware computer-readable media, e.g., as described above, havingstored thereon instructions executable by a processor to perform themethods described herein.

Example embodiments of the present invention are directed to a method,e.g., of a hardware component or machine, of transmitting instructionsexecutable by a processor to perform the methods described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a patient specific model of deep brain stimulation (DBS),where a stereotactic coordinate system was defined relative to theimaging data, microelectrode recording data were entered into the model(thalamic cells, yellow dots; subthalamic cells, green dots; substantianigra cells, red dots), a three dimensional brain atlas was fitted tothe neuroanatomy and neurophysiology (yellow volume, thalamus; greenvolume, subthalamic nucleus), and a DBS electrode was positioned in themodel, pertaining to a theoretical ellipsoid target volume, andreferring to data presented for patient No 1.

FIG. 2 shows a representative volume of tissue activated (VTA) for theRight and Left stimulators during Clinical and Model DBS settingsreferring to data presented for patient No. 1, where A) Right side Modelsettings: contact 2, 2.0V, 0.06 ms, 130 Hz, B) Left side Model settings:contact 3, 1.8 V, 0.06 ms, 130 Hz, and C) Right side Clinical settings:contact 2-3+, 4.0V, 0.06 ms, 130 Hz. D) Left side Clinical settings:contact 2, 3.2V, 0.06 ms, 130 Hz.

FIG. 3 illustrates working memory performance as percent of letterscorrectly repeated during single- and dual-task conditions, pertainingto (A) results of the n-back task in the single-task condition at OffDBS, Clinical DBS and Model DBS (Means and Standard Errors), and (B)results of the n-back task in the dual-task condition at Off DBS,Clinical DBS and Model DBS (Means and Standard Errors), and where across marks a significant differences between Off and Clinical DBS, anasterisk marks a significant difference between Off and Model DBS, and adouble asterisk marks a significant difference between Clinical andModel DBS.

FIG. 4 shows representative force-tracking trials (pertaining topatient 1) during Single (left-most column) and all Dual-task conditions(right columns) under the three DBS settings: Off (upper plots),Clinical DBS (middle plots) and Model DBS (lower plots), where thehorizontal line represents the target force line the patient wasinstructed to match.

FIG. 5 shows force-tracking performance across stimulation conditions,where (A) results of the time within the target range (TWR) of force inthe Single and Dual-task conditions at Off DBS, Clinical DBS and ModelDBS (Means and Standard Errors), and (B) results of the relative rootmean square error (RRMSE) force in the single and dual-task conditionsat Off DBS, Clinical DBS and Model DBS (mod DBS) (Means and StandardErrors), and where a cross marks a significant differences between Offand Clinical DBS, an asterisk marks a significant difference between Offand Model DBS, and a double asterisk marks a significant differencebetween Clinical and Model DBS.

FIG. 6 shows dual-task losses (DTLs) and standard errors for (a) theforce maintenance task (TWR), and (b) RRMSE at Off DBS, Clinical DBS(cli DBS), and Model DBS (mod DBS), where an asterisk signifies DTLssignificantly greater then zero and significant differences between thestates of stimulation (*p<0.05).

FIG. 7 is a flowchart showing a stimulation parameter selection method,according to an example embodiment of the present invention.

DETAILED DESCRIPTION

Bilateral deep brain stimulation (DBS) of the subthalamic nucleus (STN)is an effective therapy for improving the cardinal motor signs ofadvanced Parkinson's disease (PD) (The Deep Brain Stimulation StudyGroup, “Deep-brain stimulation of the subthalamic nucleus or the parsinterna of the globus pallidus in Parkinson's disease,” N. Engl. J. Med.345, 956-63 (2001), the entire contents of which is hereby incorporatedby reference herein). Other target sites are effective for treatingother motor, cognitive, and/or cognitive-motor disorders as outlinedabove. While bilateral STN DBS is considered safe, an emerging concernis the potential negative consequences it may have on cognitivefunctioning and overall quality of life (Freund, H. J., “Long-termeffects of deep brain stimulation in Parkinson's disease,” Brain 128,2222-3 (2005); Rodriguez-Oroz et al., 2005; Saint-Cyr, J. A. et al.,“Neuropsychological consequences of chronic bilateral stimulation of thesubthalamic nucleus in Parkinson's disease,” Brain 123 (Pt 10),2091-2108 (2000), the entire contents of each of which is herebyincorporated by reference herein). A recent report indicates patients'perceptions of their day-to-day function is improved subtly by DBS;however, caregivers perceived the patient as exhibiting subtle declinesin day-to-day functioning (Duff-Canning, S. J. et al., “He said, shesaid: Differences between self and caregiver ratings of postoperativebehavioral changes in Parkinson's disease patients undergoing bilateralsubthalamic nucleus deep brain stimulation,” In: Twelfth InternationalCongress of Parkinson's disease and Movement Disorders, vol. 23, ed.̂eds.Wiley-Blackwell, Chicago, Illinois, p. S127 (2008), the entire contentsof which is hereby incorporated by reference herein).

Several long-term studies examining changes in cognitive functionsuggest that bilateral STN DBS results in varying levels of decline inoverall cognitive functioning, including verbal fluency (Contarino, M.F. et al., “Cognitive outcome 5 years after bilateral chronicstimulation of subthalamic nucleus in patients with Parkinson'sdisease,” J Neurol Neurosurg Psychiatry 78, 248-52 (2007); Funkiewiez,A. et al., “Long term effects of bilateral subthalamic nucleusstimulation on cognitive function, mood, and behaviour in Parkinson'sdisease,” J Neurol Neurosurg Psychiatry 75, 834-9 (2004), the contentsof each of which is hereby incorporated by reference herein) and workingmemory (Rodriguez-Oroz et al., 2005; Schupbach, W. M. et al.,“Stimulation of the subthalamic nucleus in Parkinson's disease: a 5 yearfollow up,” J. Neurol. Neurosurg. Psychiatry 76, 1640-4 (2005)(hereinafter “Schupbach et al., 2005”), the entire contents of each ofwhich is hereby incorporated by reference herein). Although some ofthese long term results may be due to natural progression of PD, theyprovide compelling evidence to suggest that bilateral STN DBS mayadversely affect different features of cognitive functioning and bringinto question earlier views that STN DBS does not impair cognition. Forexample, measures of verbal fluency and learning and memory, exhibitedsignificant declines when comparing bilateral STN DBS to pre-surgery orOFF DBS scores (OFF typically referring to the temporary turn off of DBSfor a research or clinical protocol) (Woods, S. P. et al.,“Neuropsychological sequelae of subthalamic nucleus deep brainstimulation in Parkinson's disease: a critical review,” Neuropsychol.Rev. 12, 111-26 (2002), the entire contents of which is herebyincorporated by reference herein). In a meta-analysis that included datafrom 1,398 patients with bilateral STN DBS, cognitive problems were seenin 41 percent of patients (Temel, Y. et al., “Behavioural changes afterbilateral subthalamic stimulation in advanced Parkinson disease: Asystematic review,” Parkinsonism Relat. Disord. (2006), the entirecontents of which is hereby incoporated by reference herein). Cognitiveproblems varied from a moderate deterioration in verbal memory tosignificant declines in executive functioning.

While cognitive declines are commonly seen with STN DBS, the degree ofmeasured effect may be attributable to variation in the difficulty ofthe cognitive testing across studies Hershey, T. et al., “Stimulation ofSTN impairs aspects of cognitive control in PD,” Neurology 62, 1110-4(2004), the entire contents of which is hereby incoporated by referenceherein). The majority of studies examining the cognitive effects of STNDBS have utilized relatively simple neuropsychological tests suitablefor use in a clinical environment. Therefore, reports of no or minimaleffect of STN DBS on cognitive functioning may be explained by a lack ofdifficulty in test selection or the artificial environmental, free ofdistraction, in which they are completed. Hershey and colleagues(Hershey et al., 2004) reported that bilateral STN stimulation decreasedworking memory under cognitively demanding conditions. Those results areadded to by examining cognitive and motor function individually andsimultaneously under different levels of cognitive demands (Alberts etal., 2008). As working memory demands increased, cognitive, motor andcognitive-motor function decreased during bilateral compared tounilateral STN DBS (Alberts et al., 2008). Based on the inventors'results, it is believed that the spread of current to non-motor regionsof each STN may be responsible for the disruption in cognitive, motorand cognitive-motor function during bilateral STN DBS.

Given its small size, stimulation within the STN, even with electrodecontacts located predominately within the sensorimotor territory, canresult in the spread of current to limbic and associative areas as wellas to surrounding structures and fiber systems that may also affectcognition (Maks, C. B. et al., “Deep brain stimulation activationvolumes and their association with neurophysiological mapping andtherapeutic outcomes,” J. Neurol. Neurosurg. Psychiatry 80, 659-66(2009), the entire contents of which is hereby incoporated by referenceherein). The electric field generated by DBS is non-discriminatelyapplied to all of the neural elements surrounding the electrode, andthese stimulation effects are subsequently transmitted throughout thebasal ganglia and thalamocortical networks (Asanuma, K. et al., “Networkmodulation in the treatment of Parkinson's disease,” Brain 129, 2667-78(2006); Karimi, M. et al., “Subthalamic nucleus stimulation-inducedregional blood flow responses correlate with improvement of motor signsin Parkinson disease,” Brain 131, 2710-9 (2008); Phillips, M. D. et al.,“Parkinson disease: pattern of functional MR imaging activation duringdeep brain stimulation of subthalamic nucleus—initial experience,”Radiology 239, 209-16 (2006), the entire contents of each of which ishereby incoporated by reference herein). In turn, diminished cognitivefunction may be due to nonselective activation of non-motor pathwayswithin and around the STN. However, according to the present invention,when the STN is stimulated, current spread to limbic and associativeregions as well as throughout the basal ganglia and Thalamocorticalnetworks is avoided through software modeling and calculations of thoseVTAs.

The interplay between the patient and clinician performing the DBSparameter selection is critical in defining the balance betweentherapeutic benefit and stimulation induced side effects. However,clinical DBS programming is typically done without the opportunity tovisualize the spread of stimulation relative to the surrounding anatomy.In turn, current spread into non-target areas could occur without overtclinical signs, but still result in side effects not typically testedfor in traditional clinical programming sessions. Therefore, recentlydeveloped Windows-based software tools that enable 3D visualization ofthe volume of tissue activated (VTA) by DBS as a function of thestimulation parameters and electrode location in the brain have beendeveloped (Butson, C. R. et al., “StimExplorer: deep brain stimulationparameter selection software system,” Acta Neurochir Suppl. 97, 569-74(2007) (hereinafter “Butson et al., 2007b”); Miocinovic et al., 2007,the entire contents of each of which is hereby incoporated by referenceherein). In an example embodiment, quantitative theoretical predictionsare used to define stimulation parameter settings, customized to thepatient, maximizing stimulation of target areas and minimizingstimulation spread to non-target areas.

Described herein is a comparison of the effectiveness of two DBSprogramming strategies, standard Clinical (where current is spreadthroughout the dorsal and ventral portions of the STN) and Model-based,on cognitive-motor performance in advanced PD patients under dual-taskconditions, where the primary criterion for the selection of Model DBSparameters is maximizing stimulation of target areas in the subthalamicregion while minimizing stimulation of associative/limbic (ventral)sections of the STN. The target areas were defined as the dorsal STN andwhite matter dorsal to the STN (FIG. 1) (Butson, C. R. et al.,“Patient-specific analysis of the volume of tissue activated during deepbrain stimulation,” Neuroimage 34, 661-70 (2007) (hereinafter “Butson etal., 2007a”); Maks et al., 2009). Minimizing spread of current to thenon-motor regions of the STN and focusing current spread to areaspreviously shown to produce ideal therapeutic benefit may minimizecognitive-motor declines under dual-task conditions without compromisingimprovements in motor function.

A total of 10 participants with advanced PD between the ages of 51 and72 years (mean 58.6) participated in a study. Table 1 contains patientdemographics and time since DBS surgery (DBS duration) and Table 2contains Clinical and Model DBS parameters. All patients had undergonesimultaneous bilateral STN DBS surgery at the Cleveland Clinic at least14 months prior to study participation. Surgical procedures for DBSimplantation have been reported in detail previously (Machado, A. etal., “Deep brain stimulation for Parkinson's disease: surgical techniqueand perioperative management,” Mov. Disord. 21 Suppl. 14, S247-58(2006), the entire contents of which is hereby incorporated by referenceherein). Stimulation parameters for DBS devices were clinicallydetermined using the methods described by Moro and colleagues (Moro etal., 2006) and were stable for at least six months prior to studyparticipation. The programming of stimulators was overseen by anexperienced DBS programming team consisting of a programming nurse andmovement disorders neurologist specializing in PD. Because participantsneeded to make verbal responses during the working memory test, patientswith dysarthria or speech impairment were excluded. Prior to datacollection, all participants signed an informed consent approved by theCleveland Clinic Institutional Review Board.

TABLE 1 Patient demographics and UPDRS-III scores during Off, Clinicaland Model DBS conditions and the percent change from Off to Clinical andOff to Model DBS. DBS UPDRS-III (%) duration UPDRS-III score Off to Offto Patient Gender Age (years) (months) Off Clinical Model Clinical Model1 F 52 14 61 32 35 47.54 42.62 2 M 51 40 65 30 40 53.85 38.46 3 M 54 2650 31 31 38.00 38.00 4 M 63 38 56 35 32 37.50 42.86 5 M 71 29 61 26 3057.38 50.82 6 M 53 17 44 26 18 40.91 59.09 7 M 72 35 51 31 29 39.2243.14 8 M 51 33 55 30 27 45.45 50.91 9 M 61 45 68 28 31 58.82 54.41 10 M 58 14 56 31 32 44.64 42.86 Mean 58.60 29.10 56.70 30.00 30.50 46.3346.32 SD 7.55 10.55 6.90 2.61 5.35 7.53 6.70

A 6 degree of freedom force-torque transducer (Mini-40 Model, ATIIndustrial Automation, Garner, N.C., USA) was used to measure normalforce (Fz; grip) during a force-tracking motor task. Grip force wasmeasured with a resolution of 0.06 N at a sampling rate of 128 Hz. Acustomized LabView program developed by the inventors' laboratory wasused to collect and display force data to the participant. In an exampleembodiment of the present invention, stimulation parameters may beselected based on results of a force-maintenance task test (e.g., incombination with a cognitive function test), where the force-maintenancetask test is performed using a 6 degree of freedom force-torquetransducer to measure the force. Moreover, the described resolution of0.06 N may be used at the sampling rate of 128 Hz.

The N-Back Task

Various forms of the n-back task have been used in a number of previousstudies (for comprehensive review see Owen, A. M. et al., “N-backworking memory paradigm: a meta-analysis of normative functionalneuroimaging studies,” Hum. Brain Mapp. 25; 46-59 (2005), the entirecontents of which is hereby incorporated by reference herein). Then-back task utilized in the current study was based on the methodsoriginally used in its development. This version of the n-back taskrequires the participant to repeat the nth item back (e.g., 0-back,1-back, 2-back) in a sequentially presented list of items (Dobbs, A. R.et al., “Adult age differences in working memory,” Psychol Aging 4,500-3 (1989), the entire contents of which is hereby incorporated byreference herein). This same technique was used in a recent dual-taskstudy with advanced PD patients during unilateral and bilateral STN DBS(Alberts et al., 2008). The difficulty level of the n-back task ismanipulated by requiring the participants to remember items further backin the list. The type of n-back test used in this study utilized a listof random letters presented to the participant. The number ofintervening letters varied from zero to two. This method of n-backtesting requires encoding, maintenance, updating and output. However,unlike other versions of the task it does not require comparison ordecision-making.

Two English-speaking experimenters administered the n-back task.Experimenter 1 read aloud the randomized letter sets of the n-back taskwhile experimenter 2 monitored the participant's responses for accuracy.Participants were asked to respond by articulating the letter presenteddirectly before (0-back), 1 cycle before (1-back), or two cycles before(2-back). If the participant provided an incorrect response or wasunable to answer correctly within the allotted time (1.5 s) the trialwould begin with a new sequence of letters. If the participant providesthe correct answer, additional letters may be presented for the rest ofthe 30 second trial. Approximately 19-23 trials (letters) were presentedduring a 30 second block. After performing the n-back task for 30seconds, participants rested for 15-45 seconds and then repeated then-back task under the same level of difficulty (0, 1- or 2-back).Participants performed five 30 second blocks at each n-back condition(0, 1- and 2-back). These five blocks were collected sequentially andwere randomized across participants. To account for practice effects,all participants completed three practice trials (30 seconds each) ateach n-back level prior to data collection. Three trials have been shownto be sufficient to ensure task comprehension and stable performance foradvanced PD patients (Alberts et al., 2008); all participants in thecurrent study reported task comprehension and demonstrated stableperformance. All practice and test blocks consisted of a unique list ofrandomized letters to prevent any memorization of letters. In anembodiment in which parameters are selected on a per-patient basis basedon how the patient performs during the described tests, the n-back testmay be administered and parameter selection may be based on number oftotal errors during 30 seconds, number of correct responses, and numberof letters before the first error.

Force-Maintenance Task

Participants used a precision grip (i.e., thumb and index finger only)of their dominant hand to exert an isometric force against the forcetransducer. Similarly, in an example embodiment of the presentinvention, a precision grip may be used in a force maintenance test,based on results of which stimulation parameters are selected. Theparticipant's dominant band was determined using the Edinburg HandednessInventory (Oldfield, R. C., “The assessment and analysis of handedness:the Edinburgh inventory,” Neuropsychologia. 9, 97-113 (1971), the entirecontents of each of whichis hereby incoporated by reference herein). Theforce transducer was oriented in a comfortable position to the patientand affixed to the table to prevent any movement and for consistencythroughout force tracking. Three maximum precision grip efforts, 5seconds each, were completed at each of the three data collectionsessions. These data were used to establish the maximum grip force ofthe patient. Between each maximum effort, patients rested 1-2 minutes.The peak force achieved from the three efforts was considered themaximum and was used to calculate a target force level; 20% of themaximum force. A target force level may similarly be selected foradministering a test based on which to select stimulation parameters fora patient who performs the test. The 20% target force level was selectedas Galganski and colleagues (Galganski, M. E. et al., “Reduced controlof motor output in a human hand muscle of elderly subjects duringsubmaximal contractions,” J. Neurophysiol. 69, 2108-2115 (1993), theentire contents of which is hereby incorporated by reference herein)found no differences in younger adults' and older adults' standarddeviation (SD) at this force level and based on previous studies withyounger adults, older adults and advanced PD patients this force levelcould be maintained relatively easily with minimal fatigue (Alberts etal., 2008; Voelcker-Rehage, C., Alberts, J. L., “Age-related differencein working memory and force control under dual-task conditions,” Aging,Neuropsychology, and Cognition 13, 1-19 (2006) (hereinafter“Voelcker-Rehage and Alberts, 2006”); Voelcker-Rehage, C. et al.,“Effect of motor practice on dual-task performance in older adults,” J.Gerontol B Psychol. Sci. Soc. Sci. 62, P141-8 (2007) (hereinafter“Voelcker-Rehage and Alberts, 2007”), the entire contents of each ofwhich is hereby incorporated by reference herein). The target forcelevel produced and actual real-time grip force produced by theparticipant was displayed on a 21″ LCD monitor located ˜44-59 cmdirectly in front of the participant. Participants were instructed tomatch their grip force to the target force line as accurately aspossible. An auditory stimulus “ready, go” signaled the participants tostart matching their force to the target force. Participants performedone to five practice repetitions prior to test blocks to be certain alltask requirements were understood. Ten force-maintenance blocks for eachlimb, 30 seconds each, were performed with at least 30 seconds of restbetween each block. The test administered to a patient for determiningstimulation parameters for the patient, according to example embodimentsof the present invention, may be similarly administered.

Dual Task: N-Back and Force Maintenance Simultaneoulsy

Participants performed 15 dual-task blocks in which they were asked tosimultaneously perform the n-back task and force maintenance task. Theforce maintenance task was performed in random combination with each ofthe three n-back conditions (0-back, 1-back, 2-back; five repetitionseach). Participants were instructed to perform both tasks as accuratelyas possible and to devote half of their attention to the cognitive taskand half of their attention to the motor task. Participants were givenat least 30 seconds of rest between each block. The tests for selectionof stimulation parameters on a per patient basis may be similarlyadministered.

Selection of Model DBS Parameters

For each subject enrolled in the study a patient-specific DBS computermodel of each side of the patient's brain using Cicerone v1.2, a freelyavailable academic DBS research tool (Miocinovic et al., 2007) (FIG. 1)was created. The models were created without any a priori knowledge ofthe patient, aside from access to their clinical MRI and CT imagingdata, surgical targeting data, and intra-operative microelectroderecording (MER) data. Researchers were blinded to each patient'sclinical symptoms, drug regiment, clinical DBS programming notes, andClinical stimulation parameter settings.

Each patient-specific DBS model included coupled integration of MRI/CTdata, MER data, 3D brain atlas surfaces, DBS electrodes, and VTApredictions all co-registered into the neurosurgical stereotacticcoordinate system following previously described methodology (FIG. 1)(Butson et al., 2007a; Butson et al., 2007b; Miocinovic et al., 2007,all of which are incorporated by reference herein). The first phase ofmodel development was to import imaging data into the software. Thestereotactic coordinate system was defined by identifying fiduciallandmarks of the neurosurgical head frame used to implant the electrode(FIG. 1A). The CT or MRI acquired with the frame in place was called theframe image and any subsequent imaging data used in the model wasco-registered to the frame image. Co-registration between the frameimage and an alternative image was performed manually within Ciceroneusing a two step process. First, coordinates of the anterior andposterior commissures (AC/PC), defined by the operating neurosurgeon,were used to initially register the two images together. Second, a ninepanel graphical user interface (GUI) allowed for manual manipulation tofine tune the image fusion. This GUI displayed the axial, coronal, andsaggital views of the frame image on the left column, the alternativeimage on the right column and an overlay of the two in the middlecolumn. Because the images were from the same individual a rigid bodytransformation could be performed to bring the images into near perfectalignment.

The second phase of model development consisted of entering thestereotactic location of each MER data point, color coded based on itsneurophysiologically defined nucleus, into the model (FIG. 1B,C). 3Danatomical representations of the various nuclei of interest (thalamus,subthalamic nucleus, etc.) were then scaled and positioned within thecontext of the pre-operative MRI and MER data (FIG. 1B,C). This processwas performed manually, taking into account both anatomical structuresvisible in the MRI and fitting MER points within their respectivenuclei, to provide the best possible overall fit of the brain atlas tothe patient (Lujan, J. L. et al., “Automated 3-Dimensional Brain AtlasFitting to Microelectrode Recordings from Deep Brain StimulationSurgeries,” Stereotact. Funct. Neurosurg. 87, 229-240 (2009); Maks etal., 2009). Once the patient's anatomical model was defined, theelectrode type (Medtronic Electrode Model 3387 or 3389) was selected andthe implantation position of the DBS electrode, as defined byintra-operative stereotactic coordinates, was displayed within the modelsystem (FIG. 1D). Comparison with the post-operative CT verified thatthe intended surgical placement of the DBS electrode was within theartifact of the imaged electrode.

Based on previous experience developing patient-specific models oftherapeutic STN DBS (Butson et al., 2007a; Maks et al., 2009), atheoretical ellipsoid target volume (FIG. 1E) was defined. Stimulationof this target area, which included the dorsal STN and white matterdorsal to the STN, has been associated with excellent clinical outcomesin previous work. A stimulation parameter setting was defined for eachside of each patient's brain that maximized stimulation coverage of thetarget volume and minimized stimulation spread outside of the targetvolume. This theoretically optimal parameter setting was called the“Model DBS” and it was defined using theoretical predictions of thevolume of tissue activated (VTA) (FIG. 2). The VTA provides anelectrical prediction of the volume of axonal tissue directly activatedby DBS for a given stimulation parameter setting. The VTAs used inCicerone v1.2 are pre-compiled solutions from the DBS models previouslydescribed. (Butson, C. R. et al., “Predicting the effects of deep brainstimulation with diffusion tensor based electric field models,” MedicalImage Computing and Computer Assisted Intervention, InternationalConference on Medical Image Computing and Computer Assisted Intervention9, 429-37 (2006) (hereinafter “Butson et al., 2006”), the entirecontents of which is hereby incoporated by reference). The softwareprovided the ability to quickly and interactively evaluate a wide rangeof stimulation parameter settings and enable definition of atheoretically optimal Model DBS for each side of each patient (Table 2).

TABLE 2 Clinical and model stimulation parameters for all patientsClinical Settings Model Settings Pulse Pulse Voltage Width FrequencyVoltage Width Frequency Patient Contact (V) (μs) (Hz) Contact (V) (μs)(Hz) Left Stimulation Parameters 1 2-C+ 3.2 90 130 3-C+ 1.8 60 130 22-C+ 3.2 90 185 2-C+ 2.6 60 130 3 2-3+ 3.5 60 135 2-C+ 2.3 60 130 4 2-3+3.6 60 135 2-C+ 1.8 60 130 5 2-3+ 3.6 90 135 2-C+ 2.6 60 130 6 2-C+ 3.060 130 2-C+ 2.4 60 130 7 1-3+ 3.6 90 185 2-C+ 2.5 60 130 8 1-C+ 3.2 90135 2-C+ 2.4 60 130 9 1-2-C+ 2.9 60 130 2-C+ 1.8 60 130 10 1-C+ 3.2 60185 2-C+ 2.4 60 130 Right Stimulation Parameters 1 2-3+ 4.0 90 130 2-C+2.0 60 130 2 1-C+ 3.6 60 185 2-C+ 2.2 60 130 3 1-2+ 3.5 60 135 2-C+ 2.660 130 4 1-3+ 3.3 60 135 2-C+ 2.8 60 130 5 2-3+ 3.9 90 135 2-C+ 2.8 60130 6 2-C+ 3.2 60 130 2-C+ 2.6 60 130 7 2-C+ 3.6 90 185 3-C+ 1.5 60 1308 2-C+ 3.2 60 135 2-C+ 1.8 60 130 9 1-2-C+ 2.9 60 130 2-C+ 2.4 60 130 102-C+ 3.2 60 185 2-C+ 2.0 60 130

Following completion of the clinical study, the VTAs for each patientwere quantified under both the Model and Clinical settings, along withtheir respective overlap with the STN volume. Each STN volume, as fittedto each hemisphere of each patient, was divided into a ventral anddorsal section. The STN division was defined by a plane parallel to theAC/PC plane that cut through the centroid of the STN. Table 3 containsthe total VTA for each DBS condition and the percent in the ventral anddorsal portions of the STN (remaining numbers being outside the dorsaland ventral portions).

TABLE 3 Total volume of tissue activated (VTA) during Model and ClinicalDBS and the percent of VTA within the dorsal and ventral portions of theSTN for Model and Clinical settings. Model Clinical total total PatientSide VTA dorsal ventral VTA dorsal ventral 1 Left 45 13.8 0 116.6 47.418.9 1 Right 55.1 36.2 0.5 39 23.9 0 2 Left 71.7 20.3 0 108.9 31 0.6 2Right 57.2 6.8 0 124.9 34.8 36.2 3 Left 65.4 28.2 2.3 29.1 12.3 0 3Right 76.3 24.3 0.4 39.6 10.7 8.7 4 Left 49.6 25.7 0.3 29.8 15 0 4 Right83.8 46.2 3.7 44.9 22.4 12.4 5 Left 76.4 38.9 6.4 33.6 19.5 0.2 5 Right84.1 45.2 2.4 37.4 20.5 0 6 Left 68.4 22.1 0 96.9 32.3 0 6 Right 73.727.4 0.4 106.3 37.2 2.3 7 Left 68.9 13 0 45.3 4.2 6.3 7 Right 35.4 25.33.3 137.2 35.7 35.7 8 Left 68.2 17.5 0 129.1 28.2 28.5 8 Right 49.5 25.51.7 103.7 41.3 9.5 9 Left 47.4 12 0 199.9 78.8 33 9 Right 65.5 25.8 0199.9 68.6 43.6 10  Left 64.5 21 0 106.3 39.5 37.7 10  Right 52.9 13 096.3 25.4 0 AVERAGE 63.0 24.4 1.1 91.2 31.4 13.7 STDEV 13.4 10.8 1.753.0 18.3 15.9 VTA (mm{circumflex over ( )}3)

Calculation of Power Requirements for Stimulation Parameters

Waveforms were simulated according to the specific output of theMedtronic implanted pulse generator (Butson, C. R. et al., “Differencesamong implanted pulse generator waveforms cause variations in the neuralresponse to deep brain stimulation,” Clin Neurophysiol. 118, 1889-94(2007) (hereinafter “Butson and McIntyre, 2007”), the entire contents ofwhich is hereby incorporated by reference herein). The power ofstimulation with a given frequency, pulse width, and amplitude wascalculated by averaging the instantaneous power over a 1 second period,

${P_{ta} = {\frac{1}{T}{\int_{0}^{T}{\frac{{V(t)}^{2}}{R} \cdot {t}}}}},$

where Pta is the time-averaged power, T is set to I s, V(t) is theinstantaneous voltage, R input resistance, and t is time. The powerconsumption, in microwatts, was calculated for Clinical and Model DBSsettings.

Procedure

All data were collected during two visits to a research laboratory atthe Cleveland Clinic. These two data collection sessions were separatedby at least 72 hours. For both sessions, participants reported to thelaboratory in the clinically defined off condition (i.e., at least 12hours since their last dose of antiparkinsonian medication) while on DBSwith their clinically defined stimulation parameters. After completingthe informed consent process, patients were evaluated clinically withthe Unified Parkinson's Disease Rating Scale (UPDRS) Part-III Motor Examadministered by an experienced movement disorders neurologist. The sameneurologist completed all ratings except for one experimental session(patient 9; Clinical settings).

Each participant completed evaluation and testing under three DBSconditions: Off DBS, Clinical DBS, and Model DBS across the twolaboratory visits. The order of testing Clinical and Model DBSparameters were randomized across patients across the two laboratoryvisits. For example, Day 1 testing consisted of completing all testswhile on Clinical DBS and then following completion the patient'sstimulator was turned Off for three hours and all clinical, motor,cognitive and cognitive-motor testing was repeated. On Day 2 the patientwould complete all testing using the Model DBS parameters. Five patientswere tested under Clinical DBS on Day 1 and five patients completedModel DBS on Day 1. Within each experimental session, the single taskconditions were completed before the dual-task conditions. The singletask conditions were the n-back task (three levels of difficulty: 0- 1-and 2-back) and force maintenance task only. The order of completing thesingle task cognitive and motor tasks was randomized across patients.The order of dual-task conditions, force maintenance with the threedifferent levels of n-back, was randomized across patients.

According to the embodiment where stimulation parameters are selected ona patient-specific basis based on results of such tests, the tests maybe performed initially under the stimulation settings of the predictedmodel parameters as discussed above. Subsequently, the tests may beperformed under parameters selected based on the clinician's judgment inview of the patient's performance on prior iterations of testadministration and VTA size and shape for various settings.Additionally, the tests may be administered and data may be collectedprior to programming when the patient has yet to have any stimulation,to obtain a baseline of cognitive-motor function.

The Clinical DBS and Off DBS experimental session patients completed alltesting on two occasions within the same day: first under Clinical DBSparameters and then while Off DBS. After completing all clinical,cognitive, motor and cognitive-motor tests under Clinical DBS, thepatient's stimulators were turned Off for three hours to allow theeffects of DBS to wear off (Alberts, J. L. et al., “Comparison ofpallidal and subthalamic stimulation on force control in patient's withParkinson's disease,” Motor Control. 8, 484-99 (2004) (hereinafter“Alberts et al., 2004”); Alberts et al., 2008; Temperli, P. et al., “Howdo parkinsonian signs return after discontinuation of subthalamic DBS?,”Neurolog. 60, 78-81 (2003), the entire contents of each of which ishereby incorporated by reference herein). During this three hour washout period the patient remained in the laboratory and was provided lunchand rested. Following the 3 hour wash out period, the patient repeatedall clinical, cognitive, motor and cognitive-motor tests. Uponcompletion of this experimental session, the patient's stimulators wereturned on (Clinical DBS parameters were restored) and they resumed theirantiparkinsonian medication. Approximately 30 minutes after taking theirmedication and restoration of DBS the patient departed the laboratory.The total time spent in the laboratory during a Clinical DBS and Off DBSexperimental session was approximately 5-6 hours (˜2 hours of datacollection and 3 hours rest during the wash out period).

The Model DBS experimental session, which randomly occurred on Day 1 orDay 2, was completed in approximately 4-5 hours. For the Model DBSsession, the patients arrived in the laboratory off antiparkinsonianmedication and on Clinical DBS. Upon arrival, both stimulators wereturned Off. The patient then rested in the laboratory for the next twohours. After two hours the patient was re-programmed using the Model DBSparameters. After 60 minutes under Model DBS parameters, the patientcompleted all clinical, cognitive, motor and cognitive-motor testing.Upon completion of the Model DBS testing session, the patient'sstimulators were reprogrammed to their clinically defined parameters andthey took their anti-parkinsonian medication and departed the labapproximately 30 minutes later.

Data Analysis

Force-maintenance: All force data were filtered with a phase-symmetriclow-pass filter employing Woltring's algorithm (detailed in previousstudies (Voelcker-Rehage, C., Stronge, A. J. et al., “Age-relateddifferences in working memory and force control under dual-taskconditions,” Neuropsychol. Dev. Cogn. B Aging Neuropsychol. Cogn. 13,366-84 (2006) (hereinafter “Voelcker-Rehage et al., 2006”);Voelcker-Rehage and Alberts, 2007)) using existing Matlab analysisprograms developed in the inventors' laboratory. Force data wereassessed to determine the patients' accuracy from three seconds afterthe start of the block until completion of the block; this periodallowed the patient sufficient time to achieve the target force. Thatis, test results were collected beginning after three seconds. Theprimary motor outcome variables for the force-tracking task were timewithin the target range (TWR) and relative root mean square error(RRMSE). The TWR is calculated by determining the time the patient'sforce trace is within ±2.5% of the target line, i.e. within 2.5% of theforce, such that, for example, if the target force is 5N, the TWR is thetime at which a force is maintained in the range of 4.375-5.625N. Thismay be different for each patient, based on the patient's target force.The assessment was done after the data collection so the patient was nottargeting this region specifically. The TWR provides an overall accuracymeasure of force-tracking. To account for differences in the amplitudeof the target force (e.g., inter-patient and intra-patient variabilitydue to stimulation status), the RRMSE, as defined in equation 1, wasused as a method of normalizing performance relative to force amplitude.The RRMSE is considered to reflect the overall variability offorce-tracking performance; a lower RRMSE suggests control of distalmusculature and hand functionality (Kriz, G. et al., “Feedback-basedtraining of grip force control in patients with brain damage,” Arch.Phys. Med. Rehabil. 76, 653-659 (1995); Kurillo, G. et al., “Forcetracking system for the assessment of grip force control in patientswith neuromuscular diseases,” Clin. Biomech (Bristol, Avon) 19, 1014-21(2004), the entire contents of each of which is hereby incorporated byreference herein). In the equation below, F_(T)(t) is the target forceprovided to the patient, F₀(t) is the force produced by the patient andT is the time of the block.

${R\; R\; M\; S\; E} = \sqrt{\frac{1}{T}{\sum\limits_{t = 0}^{T}\frac{( {{F_{0}(t)} - {F_{T}(t)}} )^{2}}{{\max ( F_{T} )}^{2}}}}$

TWR and RRMSE may be used according to the embodiment where test resultsare used for selection of parameters on a patient specific basis.Greater TWR reflects better performance and lower RRMSE reflects betterperformance.

N-back performance: N-back performance was measured by determining thepercentage of correct letters recalled during a 30 second block and thetotal number of errors committed during a block (Voelcker-Rehage et al.,2006).

Dual-task Analysis: To examine participants' performance under thedual-task conditions, the dual task loss (DTL) was computed using astandard measure to compare performance on single and dual-taskconditions (Lindenberger, U. et al., “Memorizing while walking: increasein dual-task costs from young adulthood to old age,” Psychol. Aging 15,417-436 (2000), the entire contents of which is hereby incorporated byreference herein). The DTLs were computed as the percentage of loss inmotor and cognitive performance during dual-task conditions relative toperformance in the single-task conditions in the following manner:

DTL_(force)=[(mean dual-task_(force)−mean baseline_(force))/meanbaseline_(force)]×100.

DTL_(n-back)=[(mean dual_(n-task)−mean baseline_(n-back))/meanbaseline_(n-back)]×100.

This is a measure that essentially determines the cost from a motor andcognitive perspective of moving from a single task to the more complexand difficult dual-task.

Statistical Analysis

Motor (RRMSE, TWR) and cognitive (percentage of correctly repeatedletters (PRL), number of errors (NE)) performance data were analyzedwith repeated measures ANOVAs (analysis of variance). Greenhouse Geyseradjustment was reported when the sphericity assumption was violated.Post-hoc contrasts (Bonferroni adjustment) were used to determinedifferences between the DBS status and level of task difficulty todetermine the conditions that were most affected by the different DBSparameter settings. Analyses were conducted separately for the motor andcognitive task. These statistical methods may be applied according tothe embodiment in which parameters are selected based on the testresults.

Two 3 (DBS condition: Off DBS, Clinical DBS, Model DBS)×3 (taskdifficulty: 0-back, 1-back, 2-back)×2 (context: single-task, dual-task)repeated measure ANOVAs were used to determine differences betweendifferent DBS parameter settings in n-back difficulty and betweensingle- and dual-task context using PRL and NE. The repeated measureANOVAs may be used when the study design is a within subject repeatedmeasure, such that multiple measures on the same patient are obtained,but under varying conditions. Additionally, two 3 (DBS condition)×4(task difficulty: force only, force at 0-back, 1-back, and 2-backdifficulty) repeated measure ANOVAs were carried out using the RRMSE andTWR scores.

To examine whether DTLs for the force maintenance task and the n-backdifficulties were significantly different from zero, a series ofone-sample t tests (test value=0) were conducted separately for each DBScondition. Repeated measures ANOVAS with corresponding post-hoc testswere used to compare the DTLs for task difficulties (0-back, 1-back,2-back) and DBS status. If there is no cost in moving from a single to adual task, then the DTL would be zero.

Results

Clinical Ratings

Table 1 contains UPDRS-III Motor scores for each patient during Off,Clinical, and Model DBS. For all patients, the UPDRS-III scoresdecreased (and lower is better) with Clinical and Model DBS compared toOff DBS. Clinical DBS, on average, resulted in a 46 percent improvementin UPDRS-III ratings (range: 37 to 58 percent) while Model DBS alsoimproved clinical UPDRS-III ratings by 46 percent (range: 38 to 59percent). Statistical analysis (t-tests for paired samples) revealedthat UPDRS-III scores for Clinical and Model DBS were significantlybetter than Off DBS (tcli-off(9)=3.90, p=0.004; tmod-off(9)=3.30,p=0.009). However, there was no statistical difference in UPDRS-IIIscores between Clinical and Model DBS settings (t(9)=0.23, p=0.820).

DBS Power Consumption

The power consumption associated with Clinical and Model parameters foreach stimulator and the total amount of power, in microwatts, isprovided in Table 4. In terms of total power consumption, the Modelparameters consume approximately 50 percent less microwatts thanClinical parameters (t_(mod-cli)(9)=8.45, p<0.0001). For all 10patients, total power consumption was less with Model compared toClinical parameters and power consumption was less with Model comparedto Clinical parameters for both the right and left stimulators.

TABLE 4 Power consumption, in microwatts, for Clinical and Modelstimulation parameters for each side and total power requirements forClinical and Model parameters. Power (μW) Clinical Model Total PatientLeft Right Left Right Clinical Model 1 122.65 191.63 25.67 31.69 314.2857.36 2 174.53 146.13 53.56 38.35 320.66 91.91 3 100.79 100.79 41.9153.56 201.58 95.47 4 106.63 89.60 25.67 62.12 196.24 87.79 5 161.19189.18 53.56 62.12 350.37 115.68 6 71.31 81.13 45.64 53.56 152.44 99.207 220.89 220.89 49.52 17.83 441.79 67.35 8 127.36 84.25 25.67 45.64211.62 71.31 9 66.63 66.63 25.67 45.64 133.27 71.31 10  115.46 115.4645.64 31.69 230.92 77.33 Mean 126.75 128.57 39.25 44.22 255.32 83.47 SD47.43 54.77 12.21 14.39 97.70 17.62

Cognitive Functioning and DBS During Single and Dual-Task Conditions

Percentage of Correct Letters (PCL): The results from the repeatedmeasures ANOVA (cf. FIG. 3) revealed that overall n-back performancedecreased with increasing task difficulty (F(2, 18)=48.422, p<0.001,η²=0.843). The main effects of DBS status (F(2, 18)=2.010, p=0.163) didnot achieve statistical significance while the main effect of context(F(2, 18)=4.879, p=0.055) approached statistical significance. The taskdifficulty×DBS condition interaction, however, was significant(F(4,18)=2.945, p=0.033, η²=0.247), resulting from a greater performancedecrease with increasing n-back difficulty for Clinical DBS than for Offand Model DBS. Performance on the 2-back during Clinical DBS wassignificantly lower than performance at Off DBS or Model DBS insingle-task conditions. As task difficulty increases as a result of anincrease in cognitive demands of the dual-task performance, declineswould be found during Clinical DBS, but not during Model DBS.

Number of Errors (NE): Errors in cognitive function were primarily dueto responding with the incorrect letter and the participant reporting toexperimenter that they did not remember the letter to be recalled. Lessthan 0.5 percent of the errors were the result of the patient notresponding within the ˜1.5 second time period. For the number of errors,the effect of task difficulty (F(2, 18)=50.381, p<0.001, η²=0.848) andthe task difficulty by context interaction (F(2, 18)=3.859, p=0.040,η²=0.300) were significant. Participants produced more errors as thedifficulty of the n-back task increased. The number of errors, however,did not significantly differ between the DBS states (F(2, 18)=0.450,p=0.644). This can occur, for example, as a function of the number ofletters presented. For example someone can perseverate on a response andnot get as many letters presented to that person.

Motor Function and DBS During Single and Dual-Task Conditions

Representative force-tracking data for an entire set from one patientfor all three DBS conditions during single and dual-task settings arepresented in FIG. 4. When performing the force-tracking task only (leftplots), Clinical and Model DBS resulted in better tracking performancecompared to Off DBS.

While patients were Off, force tracking performance became slightly morevariable as the difficulty of the dual-task increased. During ClinicalDBS, middle plots, force-tracking performance declined dramatically astask difficulty increased, in particular during the 2-back condition inwhich variability was greatest. The lower panels depict force-trackingtrials during Model DBS. In general, the consistency of force trackingwas relatively unaffected by increasing task difficulty under dual-taskconditions. The TWR and RRMSE measures were used to quantifyforce-tracking performance.

Time within Target Range (TWR): When completing the force maintenancetask only, Clinical and Model DBS conditions were significantly betterthan the Off DBS condition. As expected, motor performance tended todecrease (lower TWR) as patients moved from the single to dual-taskconditions (cf FIG. 5 a). However, the rate of decline in motorperformance differed across stimulation conditions. With Clinical DBSthe rate of motor performance decline was greater compared to thedecline under Model DBS settings. A significant interaction between DBScondition and task difficulty was present (F(6,54)=4.857, p<0.001,η²=0.351). During Off and Model DBS conditions, the slope of decline inmotor performance was similar across dual-task conditions. However,under Clinical DBS settings, TWR decreased dramatically across all taskdifficulties. Furthermore, Model DBS led to significantly better forcetracking performance as compared to Clinical DBS or Off DBS in alldual-task conditions.

Relative root mean square error (RRMSE): In general, the variability inforce tracking increased significantly as task difficulty increased,moving from single to dual-task conditions (F(1.35,27.73)=10.113,p=0.005, η²=0.529). Additionally, the force variability differed betweenthe three DBS conditions (F(2,54)=5.042, p=0.018, η²=0.359), and thegreatest variability occurred under Clinical DBS. In the dual-taskconditions, Clinical DBS resulted in significantly worse performancethan Off and Model DBS (cf. FIG. 5 b). As shown in FIG. 5 b, ClinicalDBS resulted in more variable force production across conditions; astask difficulty increased to the 2-back condition, force variability wassignificantly greater compared to Model DBS.

Dual-task losses (DTLs) different from zero: The DTLs for n-backperformance at the 0-back condition were relatively small andnon-significant across the three DBS testing conditions. Declines inn-back performance were greater when moving from the single task 1-backcondition to the dual-task 1-back condition, in particular for the OffDBS and Model DBS conditions (due to the fact that under single taskconditions n-back performance was relatively high). In study data, theDTLs associated with Clinical DBS were not significantly different fromthe DTLs associated with Model DBS. From a cognitive perspective, thecost in performance when moving from single- to dual-task conditions wasnot statistically significant for any of the stimulation conditions. Areason for this may be that, despite the fact that patients reportedattending to both tasks equally, they may have placed greater emphasisor allocated more attentional resources to performing the working memorytask compared to force-tracking.

As expected, force tracking performance did decline as task complexityincreased from single to dual-task conditions while Off DBS and underClinical and Model DBS settings. However, the declines in forcetracking, FIGS. 6 a and 6 b, were most present during Clinical DBSsettings. For TWR, the greatest declines in motor performance whenmoving from a single to dual-task were associated with Clinical DBS(Clinical DBS: t_(0-back)(9)=3.091, p=0.013; t_(1-back)(9)=3.058,p=0.014; t_(2-back)(9)=7.151, p<0.001; Model DBS: t_(0-back)(9)=0.537,p=0.604; t_(1-back)(9)=0.771, p=0.460; t_(2-back)(9)=2.363, p=0.042; OffDBS: t_(0-back)(9)=−1.542, p=0.157; t_(1-back)(9)=0.269, p=0.794;t_(2-back)(9)=2.026, p=0.073). The greatest performance decrements foreach DBS condition occurred during the most complex testing condition,2-back+force maintenance (compared to just force maintenance without then-back test), and the smallest decrement during the simplest,0-back+force maintenance (cf. FIG. 6 a) (compared to just forcemaintenance without the n-back test). That is, as complexity of the taskis increased, the quality of performance decreases, A similar pattern ofresults was present when examining the variability of force production(RRMSE): t_(0-back)(7)=3.54, p=0.01; t_(1-back)(7)=3.33, p=0.01;t_(2-back)(7)=7.42, p<0.01) (cf. FIG. 5 b). The greatest declines inmotor performance were associated with Clinical DBS(t_(0-back)(9)=−1.674, p=0.128; t_(t-back)(9)=−2.636, p=0.027;t_(2-back)(9)=−2.970, p=0.016). The DTLs in force tracking performance(RRMSE) at Off DBS were significant for all n-back conditions(t_(0-back)(9)=−3.767, p=0.004; t_(1-back)(9)=−5.023, p=0.001;t_(2-back)(9)=−4.131, p=0.003), whereas under Model DBS DTLs were notsignificant (t_(0-back)(9)=−2.014, p=0.075; t_(1-back)(9)=−2.005,p=0.076; t_(2-back)(9)=−1.924, p=0.087).

Task difficulty and Stimulation differences in DTLs: The DTLs_(n-back),in general, increased significantly as task difficulty also increased,(F(2, 18)=3.831, p=0.041, η²=0.299). However, the DTLs_(n-back) were notdifferentially affected across stimulation conditions (Off, Clinical orModel) (F(2, 18)=0.425, p=0.660).

For the DTLs_(force), a significant main effect of task difficulty forTWR was present (F(2, 18)=26.984, p<0.001, η²=0.750). As task difficultyincreased, DTLs in force maintenance also increased as shown in FIG. 6a. The loss in motor performance was relatively small for the 0-backcondition while relatively large for the 2-back dual-task condition. Asignificant main effect of stimulation (F(2, 18)=5.940, p=0.010,η²=0.398) was present. Differences between DBS states were significantin the 0-back, 1-back and 2-back conditions (significantly higher DTLswith Clinical DBS compared to Off and Model DBS). The DTLs in terms ofthe variability (RRMSE) of force production were similar to TWR aslosses in performance were greater during Clinical compared to Off andModel DBS conditions (FIG. 6 b).

Discussion

Recently, it has been shown that bilateral STN DBS disrupts PD patients'cognitive-motor functioning under dual-task conditions (Alberts et al.,2008). These DBS related declines in cognitive-motor functioning areminimized through the use of patient-specific DBS models that accountfor electrode location and the VTA. In an example embodiment, theprimary criterion for the selection of DBS parameters may be maximizedstimulation coverage of a target volume that includes the dorsal STN andwhite matter dorsal to the STN, thus minimizing stimulation of non-motorregions of the STN.

The typical clinical method of DBS programming was compared, withrespect to cognitive-motor performance in advanced PD patients, to thecomputational approach described herein for selecting DBS parametersthat minimize stimulation of non-motor regions of the STN. Clinicalassessments indicated both methods of DBS programming were effective inimproving UPDRS-III scores. However, under all dual-task conditionsmotor performance was, in general, better with Model determinedstimulation parameters compared to Clinical settings. In addition,cognitive performance (working memory) was better during modestlycomplex task conditions, using Model compared to Clinical settings.Overall, these data suggest that cognitive-motor declines associatedwith bilateral STN DBS can be mitigated through the use of software thatdepicts the VTA associated with a given parameter setting relative tothe targeted brain structure.

Referring to FIG. 7, in an example embodiment of the present invention,a system may, at step 700, output a GUI including a display of a modelof a patient anatomy, e.g., the patient's brain, co-registered with amodel of a stimulation leadwire. The brain model may be generated, forexample, by fitting a brain atlas to images of the patient's brain.Alternatively, the images themselves may be displayed. Alternatively,the system may display the images and the model co-registered with eachother.

At step 702, the system may obtain user input identifying a target VTA.The target VTA may be drawn such that it does not include more than 10%of the non-motor region of the patient brain, and specifically less than10% of globus pallidus. In an example, the target VTA may be drawn suchthat it does not include any of the non-motor region.

At step 704, the system and method may determine an estimated VTA andcorresponding stimulation parameters whose stimulation is estimated toproduce the estimated VTA, which estimated VTA most closely matches theobtained target VTA. In an example embodiment, the estimated VTAs (andcorresponding stimulation parameters) from which the system may selectmay be limited to those that do not extend outward beyond any of theperimeter of the target VTA, such that if the closest estimated VTAextends beyond the target VTA, a VTA that is less of a match but iscompletely included within the area of the target VTA would be selected.The estimated VTAs may be calculated based on predetermined functionsand/or based on a patient population as further described in the '330,'312, '340, '343, and '314 applications.

In an alternative example embodiment, the system may be initiallyconfigured with a universal target VTA drawn to the generic model whichis then mapped to the specific patient, separate input of a target VTAfor each patient not being necessary. The system may provide apatient-specific closest matching estimated VTA and associatedstimulation parameters based on the universal target VTA as applied tothe patient model and based on a currently used electrode leadwire.

The clinician may use the output parameters for bilateral DBSstimulation for the patient. Because the parameters correspond to anestimated VTA that closely matches the target VTA which does not includestimulation of non-motor regions, or at least only up to 10% of suchregions of the brain, and specifically less than 10% of globus pallidus,there would be significant improvement with respect to cognitive and/ormotor-cognitive degeneration as compared to conventional bilateral DBSstimulation.

At step 706, the system and method may display the estimated VTAoverlaid on the patient brain/leadwire model. For example the system mayremove the target VTA from display, the estimated VTA being displayed inits place.

In an example embodiment of the present invention, cognitive, motor, andcognitive-motor function of the patient may be assessed to fine tune thestimulation parameters. For example, at step 708, the stimulationparameters corresponding to the estimated VTA may be used in astimulation of the patient brain. Instead of the stimulation parameterscorresponding to the estimated VTA, the system may allow for theclinician to provide input to modify the stimulation parameters, e.g.,directly or by shifting the displayed estimated VTA or a displayedcurrent field.

While the patient undergoes such stimulation, motor and cognitive tests,e.g., the combination of the n-Back test and the force-maintenance taskas described above, may be administered. The system, at step 710, mayrecord results of such tests. For example, the system may record and/orcalculate the data corresponding to the graphs shown in FIGS. 3-6. Withrespect to force-maintenance, the system may include a force sensor thatsenses the force exerted by the patient, and may record such figures anddetermine the difference of such sensed force to a target force. Thesystem may also output audio through a speaker listing a series ofletters and may receive speech input via a microphone repeating lettersfor the n-back test. The system may compare the speech input to recordedletters that had been output to determine the correctness of the speechinput. Alternatively, a clinician may administer the tests, e.g.,offline.

If the results show a decline in motor, cognitive, and/ormotor-cognitive function, the clinician may input a new target VTA, sothat the method returns to step 702. Otherwise, the method may end.

In an example embodiment, the system may be preconfigured withpredefined metrics concerning results of the administered tests,indicating acceptable results and unacceptable results. For example, thesystem may be configured with such indications concerning TWR, RRMSE,and DTLs with respect to motor and/or cognitive skill as appropriate. Inresponse to unacceptable results, the system may (as reflected by thebroken lines) automatically cycle back to, for example, step 704 todetermine a new set of parameters and associated estimated VTA which mayimprove such patient functions. For example, the system may selectparameters that produce a VTA with less stimulation of non-motor regionsof the brain or whose edges are further from such regions of the brain.

According to an example embodiment, the system and method may record andvisually identify which explored VTAs were associated with a sideeffect. The clinician may identify a VTA for which there are subparresults of the described tests as such VTAs. Additionally oralternatively, the system may automatically record such VTAs as beingassociated with a side effect.

Such recordation may be helpful in that, for example, the system mayoutput a GUI showing explored regions and indicate which of those havebeen associated side effects, so that the clinician has more informationon which to base selection of stimulation parameters during subsequentstimulation sessions.

In an example embodiment of the present invention, after determining thestimulation parameter settings, e.g., based on automatic or manualselection of parameters corresponding to a VTA that is closest to atarget VTA that avoids the non-motor regions of the brain, and or basedon results of motor function, cognitive function, and dualmotor-cognitive function tests, a voltage of an electrode may bedecreased if a selected voltage is determined to cause a tinglingsensation in the patient stimulated with the determined stimulationparameters.

While is has been reported that when memory demands of a task wereincreased, PD patients with bilateral STN DBS exhibited deficits inworking memory (Hershey et al., 2004), it has been determined thatunilateral STN DBS has little effect on working memory as n-backperformance was similar during unilateral stimulation to that whenpatients were off DBS (Alberts et al., 2008). In the current study, withrespect to bilateral STN DBS, n-back performance at the most difficultcondition (2-back) was compromised to a greater degree under ClinicalDBS than under Model DBS or when Off DBS. These data suggest thatminimizing current spread to the non-motor regions of the STN mayalleviate some of the declines in working memory that may be associatedwith bilateral STN DBS. While the use of Model parameters did mitigateworking memory declines, compared to Clinical parameters, working memoryduring bilateral STN DBS with Model parameters was not better thanperformance during unilateral STN DBS (Alberts et al., 2008). Theobservation that cognitive functioning (working memory) duringunilateral DBS was better than bilateral STN DBS, whether Model orClinical based, provides a rationale for taking a more conservativeapproach to the implantation of DBS systems.

Therefore, according to an example embodiment of the present invention,a stimulation method may include implementing a staged DBS implantationstrategy, by initially performing unilateral DBS, assessing the impactof the unilateral DBS, e.g., on cognitive function, and subsequentlyimplanting the second side. Such a method may decrease the likelihood ofcognitive declines that can be associated with bilateral STN DBS andwhich may ultimately diminish the patient's overall quality of life. Forexample, the unilateral DBS may be determined to be sufficientlyeffective, and the bilateral DBS may be delayed for 6-12 months or evenas long as 5 years. thereby delaying the increased cognitive impairmentthat is a result of the bilateral DBS.

In the event of inconsistent usages between this document and thosedocuments incorporated by reference herein, the usage in theincorporated reference(s) should be considered supplementary to that ofthis document; and for irreconcilable inconsistencies, the usage in thisdocument controls.

The above description is intended to be illustrative, and notrestrictive. Those skilled in the art can appreciate from the foregoingdescription that the present invention may be implemented in a varietyof forms, and that the various embodiments may be implemented alone orin combination. Therefore, while the embodiments of the presentinvention have been described in connection with particular examplesthereof, the true scope of the embodiments and/or methods of the presentinvention should not be so limited since other modifications will becomeapparent to the skilled practitioner upon a study of the drawings,specification, and following claims. For example, while exampleembodiments discussed in detail refer to PD patients, embodiments of thepresent invention, for example, pertaining to selection of stimulationparameters based on monitoring of cognitive function, motor function,and combination thereof, may be applied to patients having otherneuro-degenerative diseases, including neuro-motor diseases orneuro-cognitive diseases.

1. A method, comprising: using deep brain stimulation, stimulating atleast one of the zona incerta, lenticular fasciculus, and motor regionof the globus pallidus, wherein less than 10% of non-motor regions ofthe brain is stimulated as a result of the stimulation.
 2. The method ofclaim 1, wherein less than 10% of the non-motor regions of the globuspallidus is stimulated as a result of the stimulation.
 3. A method,comprising: stimulating an anatomical region in a stimulation procedurein which at least one of the zona incerta, lenticular fasciculus, andmotor region of the globus pallidus are stimulated, while current is notspread to, and therefore there is no stimulation of, any of thecorticospinal tract (CS), corticobulbar tract (CB), and the non-motorregions of the globus pallidus (GP).
 4. A method, comprising: selectingstimulation parameters for treatment of a neuro-degenerative disorderbased on results of tests of cognitive function, tests of motorfunction, and a combination of such tests, the tests being conducted onby the patient to which stimulation is to be performed using theselected stimulation parameters.
 5. The method of claim 4, wherein thestimulation is performed using implanted electrodes.
 6. The method ofclaim 4, wherein the test results are used for selection of stimulationparameters that minimize creep of current to non-motor anatomic regions.7. The method of claim 6, wherein an n-Back test is used as the test fortesting cognitive function, a force-maintenance task is used as the testfor testing motor function, and a test where the patient is subjected toboth the n-Back test and the force-maintenance task simultaneously isused as the combination.